Explanation Based Learning
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Extending Explanation Based Learning By Generalizing The Structure Of Explanations
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Author : Jude W. Shavlik
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-07-10
Extending Explanation Based Learning By Generalizing The Structure Of Explanations written by Jude W. Shavlik and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-10 with Computers categories.
Extending Explanation-Based Learning by Generalizing the Structure of Explanations presents several fully-implemented computer systems that reflect theories of how to extend an interesting subfield of machine learning called explanation-based learning. This book discusses the need for generalizing explanation structures, relevance to research areas outside machine learning, and schema-based problem solving. The result of standard explanation-based learning, BAGGER generalization algorithm, and empirical analysis of explanation-based learning are also elaborated. This text likewise covers the effect of increased problem complexity, rule access strategies, empirical study of BAGGER2, and related work in similarity-based learning. This publication is suitable for readers interested in machine learning, especially explanation-based learning.
Investigating Explanation Based Learning
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Author : Gerald DeJong
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Investigating Explanation Based Learning written by Gerald DeJong and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Explanation-Based Learning (EBL) can generally be viewed as substituting background knowledge for the large training set of exemplars needed by conventional or empirical machine learning systems. The background knowledge is used automatically to construct an explanation of a few training exemplars. The learned concept is generalized directly from this explanation. The first EBL systems of the modern era were Mitchell's LEX2, Silver's LP, and De Jong's KIDNAP natural language system. Two of these systems, Mitchell's and De Jong's, have led to extensive follow-up research in EBL. This book outlines the significant steps in EBL research of the Illinois group under De Jong. This volume describes theoretical research and computer systems that use a broad range of formalisms: schemas, production systems, qualitative reasoning models, non-monotonic logic, situation calculus, and some home-grown ad hoc representations. This has been done consciously to avoid sacrificing the ultimate research significance in favor of the expediency of any particular formalism. The ultimate goal, of course, is to adopt (or devise) the right formalism.
A General Explanation Based Learning Mechanism And Its Application To Narrative Understanding
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Author : Raymond J. Mooney
language : en
Publisher: Morgan Kaufmann
Release Date : 1990
A General Explanation Based Learning Mechanism And Its Application To Narrative Understanding written by Raymond J. Mooney and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Computers categories.
By Raymond J. Mooney.
Learning Search Control Knowledge
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Author : Steven Minton
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Learning Search Control Knowledge written by Steven Minton and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
The ability to learn from experience is a fundamental requirement for intelligence. One of the most basic characteristics of human intelligence is that people can learn from problem solving, so that they become more adept at solving problems in a given domain as they gain experience. This book investigates how computers may be programmed so that they too can learn from experience. Specifically, the aim is to take a very general, but inefficient, problem solving system and train it on a set of problems from a given domain, so that it can transform itself into a specialized, efficient problem solver for that domain. on a knowledge-intensive Recently there has been considerable progress made learning approach, explanation-based learning (EBL), that brings us closer to this possibility. As demonstrated in this book, EBL can be used to analyze a problem solving episode in order to acquire control knowledge. Control knowledge guides the problem solver's search by indicating the best alternatives to pursue at each choice point. An EBL system can produce domain specific control knowledge by explaining why the choices made during a problem solving episode were, or were not, appropriate.
Explanation Based Neural Network Learning
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Author : Sebastian Thrun
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Explanation Based Neural Network Learning written by Sebastian Thrun and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.' From the Foreword by Tom M. Mitchell.
Explanation Based Learning
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-06-30
Explanation Based Learning written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Computers categories.
What Is Explanation Based Learning A type of machine learning known as explanation-based learning, or EBL for short, takes advantage of an extremely robust, or even flawless, domain theory in order to generalize from training data or construct concepts. It also has a connection with encoding, or memory, which assists with learning. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Explanation-based learning Chapter 2: Computational linguistics Chapter 3: Natural language processing Chapter 4: Corpus linguistics Chapter 5: Parsing Chapter 6: Question answering Chapter 7: Link grammar Chapter 8: Grammar induction Chapter 9: Structured prediction Chapter 10: Deep linguistic processing (II) Answering the public top questions about explanation based learning. (III) Real world examples for the usage of explanation based learning in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of explanation based learning' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of explanation based learning.
Generalizing The Structure Of Explanations In Explanation Based Learning
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Author : Jude William Shavlik
language : en
Publisher:
Release Date : 1988
Generalizing The Structure Of Explanations In Explanation Based Learning written by Jude William Shavlik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with categories.
Explanation-based learning is a recently developed approach to concept acquisition by computer. In this type of machine learning, a specific problem's solution is generalized into a form that can later be used to solve conceptually similar problems. A number of explanation-based generalization algorithms have been developed. Most do not alter the structure of the explanation of the specific problem - no additional objects nor inference rules are incorporated. Instead, these algorithms generalize by converting constants in the observed example to variables with constraints. However, many important concepts, in order to be properly learned, require that the structure of explanations be generalized. This can involve generalizing such things as the number of entities involved in a concept or the number of times some action is performed. For example, concepts such as momentum and energy conservation apply to arbitrary numbers of physical objects, clearing the top of a desk can require an arbitrary number of object relocations, and setting a table can involve an arbitrary number of guests. Two theories of extending explanations during the generalization process have been developed, and computer implementations have been created to computationally test these approaches. The Physics 101 system utilizes characteristics of mathematically-based problem solving to extend mathematical calculations in a psychologically-plausible way, while the BAGGER system implements a domain-independent approach to generalizing explanation structures. Both of these systems are described and the details of their algorithms presented. An approach to the operationality/generality trade-off and an empirical analysis of explanation-based learning are also presented.
Encyclopedia Of Machine Learning
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Author : Claude Sammut
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-28
Encyclopedia Of Machine Learning written by Claude Sammut and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-28 with Computers categories.
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Explanation Based Learning With Plausible Inferencing
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Author : Gerald DeJong
language : en
Publisher:
Release Date : 1990
Explanation Based Learning With Plausible Inferencing written by Gerald DeJong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Artificial intelligence categories.
This paper represents a synthesis of ideas from qualitative reasoning and explanation-based learning. Taken together they form a novel approach to planning that relies on plausible inferencing and applies to continuously varying rather than discrete world states. Interestingly, the frame problem skirted and the approach admits some forms of planning under uncertainty. Planning in a domain is very efficient, although learning about the domain can be time consuming. The approach possess a kind of natural reactivity. Keywords: Explanation based learning, Planning, Learning to plan, Continuous domains, Knowledge level learning. (SDW).
Abductive Explanation Based Learning A Solution To The Multiple Explanation Problem
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Author : Rutgers University. Dept. of Computer Science
language : en
Publisher:
Release Date : 1989
Abductive Explanation Based Learning A Solution To The Multiple Explanation Problem written by Rutgers University. Dept. of Computer Science and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Expert systems (Computer science) categories.
This paper proposes an extension of explanation based learning, called abductive explanation based learning (A-EBL) which solves the multiple explanation problem. A-EBL uses set covering techniques to select a minimal subset of the set of possible explanations of a training example: it is shown by formal-analysis that selection of this subset leads to convergence properties that are roughly equivalent to standard EBL. A-EBL also proven to be is computationally efficient, assuming a tractable domain theory. Experimental results are also reported on an application of A-EBL to learning correct bidding rules given an imperfect domain theory for opening bids in the game of contract bridge."